






from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
  import PIL

# soft dependency
if is_pytesseract_available():
  import pytesseract

SCREAMING_SNAKE_CASE							=    logging.get_logger(__name__)

def   _lowerCamelCase						(    __A     :				List[str]		,		__A     :				Optional[int]		,		__A     :				Any			)   ->       Dict:
    return [
        int(1_000 * (box[0] / width)			),
        int(1_000 * (box[1] / height)			),
        int(1_000 * (box[2] / width)			),
        int(1_000 * (box[3] / height)			),
    ]

def   _lowerCamelCase						(    __A     :				np.ndarray		,		__A     :				Optional[str]		,		__A     :				Optional[str] = None			)   ->       str:
    _UpperCAmelCase					:   str      	=					tesseract_config if tesseract_config is not None else ''''''

    # apply OCR
    _UpperCAmelCase					:   Union[str, Any]      	=					to_pil_image(__A			)
    _UpperCAmelCase					:   int      	=					pil_image.size
    _UpperCAmelCase					:   Optional[Any]      	=					pytesseract.image_to_data(__A		,		lang=__A		,		output_type='''dict'''		,		config=__A			)
    _UpperCAmelCase					:   Dict      	=					data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

    # filter empty words and corresponding coordinates
    _UpperCAmelCase					:   Any      	=					[idx for idx, word in enumerate(__A			) if not word.strip()]
    _UpperCAmelCase					:   int      	=					[word for idx, word in enumerate(__A			) if idx not in irrelevant_indices]
    _UpperCAmelCase					:   str      	=					[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
    _UpperCAmelCase					:   List[Any]      	=					[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
    _UpperCAmelCase					:   List[Any]      	=					[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
    _UpperCAmelCase					:   Tuple      	=					[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    _UpperCAmelCase					:   Tuple      	=					[]
    for x, y, w, h in zip(__A		,		__A		,		__A		,		__A			):
        _UpperCAmelCase					:   Tuple      	=					[x, y, x + w, y + h]
        actual_boxes.append(__A			)

    # finally, normalize the bounding boxes
    _UpperCAmelCase					:   int      	=					[]
    for box in actual_boxes:
        normalized_boxes.append(normalize_box(__A		,		__A		,		__A			)			)

    assert len(__A			) == len(__A			), "Not as many words as there are bounding boxes"

    return words, normalized_boxes


class 				A_     (    __lowercase      ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE				:      Dict			=       ["pixel_values"]


 def __init__(						self				,	_A = True				,	_A = None				,	_A = PILImageResampling.BILINEAR				,	_A = True				,	_A = None				,	_A = ""				,	**_A				,	)			->		None:

     """simple docstring"""



     super().__init__(**_A)
     _UpperCAmelCase					:   Optional[Any]      	=					size if size is not None else {'''height''': 224, '''width''': 224}
     _UpperCAmelCase					:   str      	=					get_size_dict(_A)

     _UpperCAmelCase					:   List[str]      	=					do_resize
     _UpperCAmelCase					:   int      	=					size
     _UpperCAmelCase					:   Any      	=					resample
     _UpperCAmelCase					:   Any      	=					apply_ocr
     _UpperCAmelCase					:   Tuple      	=					ocr_lang
     _UpperCAmelCase					:   Dict      	=					tesseract_config


 def 			snake_case__					(						self				,	_A				,	_A				,	_A = PILImageResampling.BILINEAR				,	_A = None				,	**_A				,	)			->		np.ndarray:

     """simple docstring"""



     _UpperCAmelCase					:   Union[str, Any]      	=					get_size_dict(_A)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}''')
     _UpperCAmelCase					:   str      	=					(size['''height'''], size['''width'''])
     return resize(_A				,	size=_A				,	resample=_A				,	data_format=_A				,	**_A)


 def 			snake_case__					(						self				,	_A				,	_A = None				,	_A = None				,	_A = None				,	_A = None				,	_A = None				,	_A = None				,	_A = None				,	_A = ChannelDimension.FIRST				,	**_A				,	)			->		PIL.Image.Image:

     """simple docstring"""



     _UpperCAmelCase					:   Union[str, Any]      	=					do_resize if do_resize is not None else self.do_resize
     _UpperCAmelCase					:   Union[str, Any]      	=					size if size is not None else self.size
     _UpperCAmelCase					:   List[Any]      	=					get_size_dict(_A)
     _UpperCAmelCase					:   Tuple      	=					resample if resample is not None else self.resample
     _UpperCAmelCase					:   Dict      	=					apply_ocr if apply_ocr is not None else self.apply_ocr
     _UpperCAmelCase					:   Dict      	=					ocr_lang if ocr_lang is not None else self.ocr_lang
     _UpperCAmelCase					:   Any      	=					tesseract_config if tesseract_config is not None else self.tesseract_config

     _UpperCAmelCase					:   Any      	=					make_list_of_images(_A)

     if not valid_images(_A):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.''')

     if do_resize and size is None:
         raise ValueError('''Size must be specified if do_resize is True.''')

     # All transformations expect numpy arrays.
     _UpperCAmelCase					:   List[Any]      	=					[to_numpy_array(_A) for image in images]

     if apply_ocr:
         requires_backends(self				,	'''pytesseract''')
         _UpperCAmelCase					:   str      	=					[]
         _UpperCAmelCase					:   List[Any]      	=					[]
         for image in images:
             _UpperCAmelCase					:   Optional[int]      	=					apply_tesseract(_A				,	_A				,	_A)
             words_batch.append(_A)
             boxes_batch.append(_A)

     if do_resize:
         _UpperCAmelCase					:   Optional[int]      	=					[self.resize(image=_A				,	size=_A				,	resample=_A) for image in images]

     # flip color channels from RGB to BGR (as Detectron2 requires this)
     _UpperCAmelCase					:   Any      	=					[flip_channel_order(_A) for image in images]
     _UpperCAmelCase					:   Optional[int]      	=					[to_channel_dimension_format(_A				,	_A) for image in images]

     _UpperCAmelCase					:   Any      	=					BatchFeature(data={'''pixel_values''': images}				,	tensor_type=_A)

     if apply_ocr:
         _UpperCAmelCase					:   Any      	=					words_batch
         _UpperCAmelCase					:   Optional[Any]      	=					boxes_batch
     return data












'''simple docstring'''





def __UpperCAmelCase   (					A					:			list		)    ->   list:
			if len(A		) <= 1:
						return lst

			UpperCAmelCase_      :       List[str]          =       1

			while i < len(A		):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												UpperCAmelCase_      :       Dict          =       1

			return lst


if __name__ == "__main__":
						_UpperCamelCase						:						int  =					input('Enter numbers separated by a comma:\n').strip()
						_UpperCamelCase						:						Union[str, Any]  =					[int(item) for item in user_input.split(',')]
						print(gnome_sort(unsorted))


